赋能视障人士：Apple Live Photos与Android Motion Photos的新颖应用
Empowering Visually Impaired Individuals: A Novel Use of Apple Live Photos and Android Motion Photos
AI总结：
针对视障人士拍摄图像质量欠佳的问题，本研究提倡使用Apple Live Photos和Android Motion Photos技术，实验证明其在物体分类和VideoQA任务中优于单帧图像。
摘要：
目前已开发出许多旨在帮助视障人士的应用程序，这些程序通过机器学习单元来处理视觉输入。然而，这些应用面临的一个关键挑战是用户拍摄的图像质量欠佳。考虑到视障人士操作相机的复杂性，我们提倡使用Apple Live Photos和Android Motion Photos技术。在本研究中，我们提出了一种直接的方法，以评估和对比Live/Motion Photos与传统基于图像的方法的有效性。研究结果表明，在常见的视觉辅助任务中，特别是在物体分类和VideoQA方面，Live Photos和Motion Photos的表现均优于单帧图像。我们通过在ORBIT数据集上进行的大量实验验证了结果，该数据集包含由视障人士收集的视频。此外，我们还进行了一系列消融研究，以深入探讨去模糊和更长的时间裁剪的影响。
英文摘要：
Numerous applications have been developed to assist visually impaired individuals that employ a machine learning unit to process visual input. However, a critical challenge with these applications is the sub-optimal quality of images captured by the users. Given the complexity of operating a camera for visually impaired individuals, we advocate for the use of Apple Live Photos and Android Motion Photos technologies. In this study, we introduce a straightforward methodology to evaluate and contrast the efficacy of Live/Motion Photos against traditional image-based approaches. Our findings reveal that both Live Photos and Motion Photos outperform single-frame images in common visual assisting tasks, specifically in object classification and VideoQA. We validate our results through extensive experiments on the ORBIT dataset, which consists of videos collected by visually impaired individuals. Furthermore, we conduct a series of ablation studies to delve deeper into the impact of deblurring and longer temporal crops.
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